Circuit Design Generator

ABSTRACT

Systems and methods for designing reconfigurable integrated circuits receive target data and training data; and generate a circuit design for implementing the target data which is over-provisioned with respect to the target data according to the training data.

RELATED APPLICATIONS

This application claims priority under 35 U.S.C. §119 and is entitled tothe filing date of UK patent application GB 1414230.1 filed on Aug. 11,2014. The contents of the aforementioned application are incorporated byreference herein.

BACKGROUND

The present disclosure relates to a circuit design generator, and inparticular to systems and methods for designing reconfigurableintegrated circuits.

The hardware industry has traditionally relied on Moore's law, theobservation that the number of transistors on integrated circuitsdoubles approximately every two years, to pack more features incomputing platforms every year. Each new generation of lithography hasallowed more hardware to be fitted in a given die size (and thereforeprice point).

Fixed hardware solutions offer the best performance and lowest powerconsumption. However as we approach the end of Moore's law, we arebeginning to see a slow-down in the shrinking of hardware from onegeneration to the next. This makes it desirable to share the samehardware for multiple tasks, and this has been a primary driver ofreconfigurable hardware.

Various reconfigurable architectures have been proposed. There are alsomoves towards coarse-grained architectures in field-programmable gatearrays (FPGAs) and other reconfigurable designs in order to increasepower efficiency and suitability for the mobile markets.

These designs may have heterogeneous cells but a common feature is afixed homogenous interconnect system that is independent of the targetapplications. Although this enables the design to run a wide variety ofapplications, they also provide flexibility beyond what is needed ifthey are to be used for a specific family of applications. Thisincreased flexibility comes at the cost of a circuit that is larger thannecessary and an interconnect structure that is slower and morepower-consuming than necessary.

Existing reconfigurable architectures are optimised manually to suit afamily of applications, and this optimisation is typically onlyrestricted to cell/node types, counts and their positions.

SUMMARY

Aspects of the present disclosure teach certain benefits in constructionand use which give rise to the exemplary advantages described below.

According to a first aspect of the disclosure there is provided a methodof generating a circuit design comprising receiving target data andtraining data; and generating a circuit design for implementing thetarget data and which is over-provisioned with respect to the targetdata according to the training data.

A circuit design for implementing the target data will be capable ofimplementing applications defined by the target data, but will not haveflexibility beyond those applications. A circuit design that isover-provisioned with respect to target data will be able to implementthe target data but may also give the circuit the capability to bemodified to have additional capability.

Optionally, an objective function is received and said over-provisionedcircuit design optimises the objective function.

Optionally, the method gathers statistics from the training data anddetermines added cell types and/or connection patterns whichover-provision the circuit design with respect to the target data.

Optionally, the method comprises generating a circuit design forimplementing the target data and then modifying that circuit design toderive the over-provisioned circuit design.

Optionally, the method comprises receiving one or more physical designconstraints and where said generated circuit design is made to complywith the physical design constraints.

Optionally, the physical design constraints comprise one or more of thearea, delay (speed), or power consumption of the circuit.

Optionally, the physical characteristics of different cells anddifferent interconnects pre-calculated and stored, and used for ensuringsaid generated circuit design complies with the physical designconstraints.

Optionally, the target data comprises netlists, and said netlists aremerged to find a minimum number of nodes required to run theapplications of the target data together with a correspondinginterconnect layout, to provide a circuit design for implementing thetarget data.

Optionally, merging the netlist comprises inputting a first netlist anda second netlist, determining the smallest compound netlist that canimplement both the input netlists; and repeating in cases where thereare two or more netlists.

Optionally, determining the smallest compound netlist that can implementboth the input netlists comprises computing a list of the unique nodetypes that appear in both the input netlists, and counting the number ofoccurrences of each type.

Optionally, the number of nodes of a given type in the output netlist isset as the maximum number of occurrences that appears in the inputnetlists.

Optionally, the training data comprises a set of existing netlists thatdo not form part of the target data.

Optionally, nodes and edges are added to the circuit design forimplementing the target data in order to generate the over-provisionedcircuit design.

Optionally, optimising the objective function comprises scoring acandidate circuit design by examining the distribution of the nodes andedges in the candidate circuit design and comparing the distributionswith the training data.

Optionally, a user can specify a node trade-off parameter forcontrolling the number of added nodes, adjustable between a firstextreme where no additional nodes are added and a second extreme wherethe distribution of the nodes and edges in the candidate circuit designexactly matches the distribution of the nodes and edges in the trainingdata.

Optionally, optimising the objective function comprises scoring acandidate circuit design by estimating a likelihood of the candidatecircuit design being able to implement functions represented by thetraining data.

Optionally, nodes in the generated circuit design are placed to minimisethe distance between those that lie on the critical paths of each targetand training data.

Optionally, the nodes are placed according to an integer linear program,in which the variables being optimised are assignments of nodes to aposition in a grid.

Optionally, placing the nodes comprises identifying the critical pathsof each input netlist, minimising a distance between nodes for thenetlist having the longest critical path; and repeating iteratively forsuccessive netlists in decreasing order of critical path length.

Optionally, a number of the next longest paths in each application arecalculated to check that the critical path before rearrangement is stillthe critical path; and the node placement is finalised if the criticalpath before rearrangement is still confirmed as the critical path.

Optionally, the number of the next longest paths is a user-adjustableparameter.

Optionally, the node placement process terminates after a set number ofiterations.

Optionally, register nodes are formed as independent nodes in an arrayof the generated circuit design.

Optionally, register nodes are formed as integrated components of otherlogic nodes.

Optionally, the method comprises deciding whether to form one or moreregister nodes as either independent nodes in the array or as integratedcomponents of other logic nodes.

Optionally, the generated circuit design comprises pipelined netlists.

Optionally, the pipelining is optimised by taking into account nodepositions and wire delays.

According to a second aspect of the disclosure there is provided acircuit design generator comprising means for receiving target data andtraining data; and means for generating a circuit design forimplementing the target data and which is over-provisioned with respectto the target data according to the training data.

The circuit design generator may comprise means for carrying out anysteps of the methods mentioned above.

According to a third aspect of the disclosure there is provided acomputer program product comprising instructions that when executed by acomputer cause it to function as the circuit design generator of thesecond aspect.

The computer program product may be stored on or transmitted as one ormore instructions or code on a computer-readable medium.Computer-readable media includes both computer storage media andcommunication media including any medium that facilitates transfer of acomputer program from one place to another. A storage media may be anyavailable media that can be accessed by a computer. By way of examplesuch computer-readable media can comprise RAM, ROM, EEPROM, CD-ROM orother optical disk storage, magnetic disk storage or other magneticstorage devices, or any other medium that can be used to carry or storedesired program code in the form of instructions or data structures andthat can be accessed by a computer. Also, any connection is properlytermed a computer-readable medium. For example, if the software istransmitted from a website, server, or other remote source using acoaxial cable, fibre optic cable, twisted pair, digital subscriber line(DSL), or wireless technologies such as infra-red, radio, and microwave,then the coaxial cable, fibre optic cable, twisted pair, DSL, orwireless technologies such as infra-red, radio, and microwave areincluded in the definition of medium. Disk and disc, as used herein,includes compact disc (CD), laser disc, optical disc, digital versatiledisc (DVD), floppy disk and blu-ray disc where disks usually reproducedata magnetically, while discs reproduce data optically with lasers.Combinations of the above should also be included within the scope ofcomputer-readable media. The instructions or code associated with acomputer-readable medium of the computer program product may be executedby a computer, e.g., by one or more processors, such as one or moredigital signal processors (DSPs), general purpose microprocessors,ASICs, FPGAs, or other equivalent integrated or discrete logiccircuitry.

Other features and advantages of aspects of the present disclosure willbecome apparent from the following more detailed description, taken inconjunction with the accompanying drawings, which illustrate, by way ofexample, the principles of aspects of the disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosure will be described below, by way of example only, withreference to the accompanying drawings, in which:

FIG. 1 shows a circuit design generator according to an embodiment ofthe disclosure;

FIG. 2 shows an example of how two target netlists are merged, as partof a circuit design process according to an embodiment of thedisclosure;

FIG. 3 shows a comparison of the merging of the netlists according tothe process illustrated in FIG. 2 with the merging of the netlists by aunion compound;

FIG. 4 shows a comparison of the area required to implement a set ofnetlists merged according to the process illustrated in FIG. 2 ascompared with being merged according to other techniques;

FIG. 5 shows an average number of nodes of different types which areencountered in a given set of training data which is used as part of acircuit design method according to an embodiment of the disclosure;

FIG. 6 shows node edges which are encountered in the same given set oftraining data from which the data of FIG. 5 is derived;

FIG. 7 shows a ratio of added nodes and their variation according to anoptimisation parameter;

FIG. 8 shows a mismatch between the circuit's distribution of nodes andthe training data distribution as a function of the optimisationparameter;

FIG. 9 shows a distribution of register cells in which the registercells are distributed in an array with each node having its own crossbarinterconnect;

FIG. 10 shows a distribution of register cells in which the registercells are integrated into the interconnect of other larger nodes at theinput of the node;

FIG. 11 shows a distribution of register cells in which the registercells are integrated into the interconnect of other larger nodes atinputs of the interconnect itself;

FIG. 12 shows the effect on a connection graph of using registers asindependent nodes versus integrating them within the crossbarinterconnect of other larger nodes;

FIG. 13 shows register nodes that are connected in series to form achain that is present in pipelined datapaths of certain applications;and

FIG. 14 shows several ways of dealing with long register chains used inpipelined datapaths.

The above described drawing figures illustrate aspects of the disclosurein at least one of its exemplary embodiments, which are further definedin detail in the following description. Features, elements, and aspectsof the disclosure that are referenced by the same numerals in differentfigures represent the same, equivalent, or similar features, elements,or aspects, in accordance with one or more embodiments.

DETAILED DESCRIPTION

A reconfigurable circuit comprises logic cells which perform basicoperations, internal memory cells known as registers and an interconnectlayer that can be selectively configured to couple logic cells and/ormemory registers with each other to create or perform various desiredfunctionalities. The registers may be incorporated as part of the logiccells or may be provided as dedicated cells. It is also possible forregister cells to be omitted altogether although this is rare inpractical systems.

An application can be modelled as a graph in which case the cells (logicor register) may also be known as nodes and the connections betweennodes, and between nodes and other circuit elements, are known as edges.

The logic cells comprise circuitry for performing basic operations,which are re-used as part of more specific or complex functionalities. A“fine-grained” reconfigurable circuit normally comprises one-bitprimitive operations (look-up tables), whereas a “coarse-grained”circuit normally comprises primitive operations that operate on a largerbit-width (for example 16-bit arithmetic). Coarse-grained operations canbe arbitrarily complex, for example a complete fast Fourier transform(FFT) or other custom accelerator, or can perform 32-bit multiplicationor division or different shifting (shift left/right). Also, areconfigurable circuit may comprise a mix of fine-grained andcoarse-grained components.

The interconnect layer comprises a plurality of interconnects which arephysical structures providing an electrical coupling between logiccells, registers and other circuit components. The interconnects maycomprise crossbar or island style switches for example. Crossbarinterconnects comprise a grid of conductors with a matrix of switches attheir intersections that selectively short-circuit the conductors toconnect one or many inputs to one or many outputs. Island interconnectscomprise switch blocks and/or connection blocks amongst the logic cellswhich selectively couple conductors provided at the inputs and outputsof the switch blocks, connection blocks and logic cells.

The present disclosure provides a solution that allows a user tooptimise the design of a reconfigurable circuit. The reconfigurablecircuit may be a complete integrated circuit (also referred to as achip), or a part thereof. The reconfigurable circuit could be anintellectual property (IP) block, being a circuit that can be used in avariety of different contexts and/or as part of larger chip designs.

FIG. 1 shows an overview of a circuit design generator 100 according toan embodiment of the disclosure. The generator 100 receives targetapplications, training applications, an objective function andoptionally design constraints and processes them to output a circuitdesign that is optimised for a specific set of related applications.

The circuit design that the generator outputs comprises a description ofan architecture for a circuit, including the cells it contains and howthey are connected. The output may comprise a set of netlists in anysuitable hardware description language, or a graphical representation.The output can be used by simulators to test the performance of acircuit made according to the design, or by synthesisers which cancontrol circuit fabrication hardware to make a circuit according to thedesign. The circuit design may also be referred to as an architecture.

The user first inputs target data, which comprises a specification ofone or more applications that the resulting circuit needs to run. Thetarget data is analysed to determine a circuit design that is capable ofrunning all the applications specified by the target data. This isreferred to as a target-ready circuit.

The target data may comprise one or more netlists associated with the oreach application, and analysing the target data may comprise merging thenetlists to determine a minimum number of nodes required to run all theapplications and a corresponding node and interconnect layout.

Graph-theoretical techniques may be used to merge the target datanetlists. Each netlist can be represented by a directed graph—a set ofvertices connected by edges, where the edges have a direction associatedwith them. Depending on the specific embodiment, the directed graph mayeither be acyclic or non-acylic. In an acyclic directed graph, each edgeconnects one vertex to another, such that there is no way to start atsome vertex v and follow a sequence of edges that eventually loops backto v again. When a directed graph is not acyclic, it is possible to loopback to the same register cell. In cases where this is done, internalregisters may be used to ensure that this loop back is not acombinatorial loop.

An algorithm for merging the netlists may comprise a sub-routine whichwe call GRAPH-SUM. The GRAPH-SUM sub-routine takes specifications of twonetlists, A and B, as an input, and computes the smallest compoundnetlist, C, that is capable of implementing the input netlists. C is thegraph with the smallest number of nodes and edges that contains both Aand B as sub-graphs.

The GRAPH-SUM subroutine first computes a list of unique node types thatappear in both A and B. For each unique node type t, it counts thenumber of occurrences of that type in the inputs. The number of nodes oftype t required in output netlist C is precisely tc=max(tA, tB) of thecounts in A and B, to and tB.

One means of representing directed graphs is via an adjacency matrix.Specifically, the adjacency matrix of a simple directed graph on nvertices is the n×n matrix in which the non-diagonal entry at positioni, j is 1 if there exists an edge from vertex i to vertex j, and 0otherwise. It is also possible to represent the directed graphs via anincidence matrix or by various other methods.

GRAPH-SUM proceeds by converting input netlists A and B into adjacencymatrix form (matrix_(A) and matrix_(B)). Once in this form, bothmatrices are padded with all-0 rows and columns such that they containthe same number of nodes of each type as C (calculated above). In otherwords, extra nodes are added to A and B's graphs to ensure that theycontain as many nodes of each type as the compound netlist C. Note thatat this stage these newly-added nodes are not connected to the remainderof the graph via edges.

The compound graph C with the smallest number of edges that contains Aand B as sub-graphs is specified by adjacency matrixmatrix_(C)=matrix_(A)|matrix_(B), where | is the element-wise OR of thetwo adjacency matrices. This operation can be performed on the twomatrices as they are both of the same size (after padding), and bothcontain only 0s or 1s.

FIG. 2 shows an example of how two target netlists datapath 1 anddatapath 2, are merged. The netlists are represented in the figure asgraphs and the merged graph on the right hand side of the figurerepresents a target-ready circuit capable of running both applications(or components of applications) which are represented by datapath 1 anddatapath 2.

In the figure, “source” denotes an input that is received from another(external) component; “reg” denotes a register; “mul” denotes amultiplication operation; “add” denotes an addition operation; “sink”denotes an output that is sent to another (external) component.

An arrow with arrow head indicates a connection between components, withthe arrowhead indicating the sequence of operation.

GRAPH-SUM is both associative and commutative. This allows us to computethe smallest netlist capable of implementing a collection of (more thantwo) input netlists by repeatedly applying the GRAPH-SUM routine. Theresult of this repeated application is an architecture referred to asthe target-ready circuit, which can run all the applications specifiedby the target data.

The analysis of target data to obtain a target-ready circuit design wastested with three training netlists: an RGB (red-green-blue colourspace)to YUY2 (Y luminance and U chrominance colourspace) converter, abilinear demosaicer, and a box blur image filter.

FIGS. 3 and 4 illustrate a comparison of the merging of the netlistsaccording to the disclosure with a baseline of a naive union compoundwhere all target netlists are placed side by side in silicon. Acomparison is also made with the smallest circuit capable of running thethree netlists produced according to the methods taught in WO2006/114642, referred to as RICA (reconfigurable instruction cell array)for convenience. The results are summarised in FIGS. 3 and 4. We observethat the graph according to the present disclosure implements the targetnetlists using significantly less space than both the union compound andRICA.

FIG. 3 shows node and edge counts. The target data are represented asgraphs 1-3, and represent a bilinear demosaicer, a box blur imagefilter, and an RGB to YUY2 converter respectively. The figure shows thenumber of nodes and edges used by a compound graph according to thedisclosure, referred to as a SMART compound graph, compared with a naiveunion of all target data. The SMART compound graph and the unioncompound graph are both able to execute all the target algorithms,however the SMART compound graph does so using fewer nodes.

FIG. 4 shows the area required to implement each of the three sametarget algorithms (again labelled as graphs 1-3) compared with that ofthe SMART compound graph, a RICA graph and a crossbar graph sufficientlylarge to implement the target algorithms. The SMART graph achieves thisusing significantly less space. Note the log scale on the y axis.

The target-ready circuit design represents an efficient design forrunning a minimum set of required applications. However the circuitdesign can be further improved according to the disclosure by designingan architecture that is over-provisioned compared to the target data(which represent the algorithms that the circuit must run), so that itis likely to also be able to run certain expected advancements in thoseapplications over the near-term future. This may be known as afuture-proof circuit.

One approach would be to add a large number of extra nodes and edges tothe target-ready circuit design to increase the circuit's capacity.Alternatively, future algorithms can be predicted in order tointelligently over-provision the circuit architecture. It is impossibleto predict the future with complete certainty. However, one sensible wayof making predictions is to examine existing netlists that might beexpected to run on the circuit in future years but are not currently inthe target set. The training data may comprise this collection ofnetlists.

The training data comprises data such as a set of netlists which isintended to be used to improve a target-ready circuit (design) by makingit more future-proof. The netlists may specify one or more applicationsor features that the resulting future-ready circuit should be able torun at some point in the future, or modifications to the applications orfeatures that are specified by the target data. Training data may bechosen to be representative of aspects of algorithms that are mostlikely to increase in complexity.

It is the user's responsibility to choose these netlists to berepresentative of aspects of algorithms that are likely to be run on thecircuit in the future. The generator goes through the training data anduses statistics gathered from this dataset to over-provision the circuitby adding cell types and connection patterns extracted from the trainingdata. The objective function is the tool that the circuit designer usesto express the way in which the statistics of the training data affectthe over provisioning of the circuit.

An objective function may be any quantitative measure of howfuture-proof a given circuit is. This objective function normally takesinto account the training applications provided by the user. Forexample, the objective function could identify repeated units in a graphand make the assumption that algorithm families tend to advance byoperating on larger regions of neighbouring data from one generation tothe next, so these repeated units should be made more abundant.Similarly, the repeated unit itself may become more complicated, soavailability of common operations that could be added to the chain wouldbe a benefit for future readiness.

A user can also define one or more constraints that define mandatorycharacteristics of the resultant future-ready circuit. The constraintsmay be physical characteristics such as the area of the circuit, delay(speed), or power consumption.

Characteristics of individual nodes and different types and/or sizes ofinterconnects may be pre-calculated and stored in order to enforce thespecified constraints. Storage may be provided via an appropriate memoryforming part of the circuit design generator 100.

The future-ready circuit may be formed by modifying the design of thetarget-ready circuit according to the training data and any definedconstraints. However in an alternative embodiment a future-ready circuitdesign can be generated directly from inputs comprising target data,training data and any constraints.

The objective function is then used to guide an optimisation routine,which adds nodes and/or edges to the target-ready circuit in order toincrease the value of the objective function (i.e. to make the circuitmore future-proof).

One suitable objective function which can be provided according to anembodiment of the disclosure comprises allocating a score to a circuitdesign by examining the distribution of the nodes and edges in thecircuit (for example histograms of counts) and comparing thesedistributions to those observed in the training data. The more similarthese distributions, the more future-proof that circuit is.

This can be referred to as a node-edge objective function, and anexample of its implementation is illustrated with respect to FIGS. 5 to8, with a training dataset that comprises five netlists (an epsilonfilter, a manual filter and three multipass filters).

FIG. 5 shows the distribution of nodes in a training dataset. The figureshows the average number of nodes of each type encountered in thetraining data. Bars indicate one standard deviation and demonstrate thevariability in the node counts. In the figure, ALU represents anarithmetic logic unit, mul represents a multiplier cell, mux representsa multiplexer cell, reg represents a register cell, sbuf represents abuffer cell (memory elements), sink represents a cell that allows datato be outputted to another (external) component, and source represents acell that allows data to be inputted from another (external) component.

FIG. 6 shows the distribution of edges in the same dataset. Thebrightness of a cell in row i and column j is proportional to the lognumber of times an edge was observed between a node of type i and j inthe training data.

The distribution of nodes and edges in the target-ready circuit willalmost certainly be different. By adding a sufficient number of nodesand edges to the target-ready circuit, it will always be possible tomatch the distribution of nodes in the training data. We define a nodetrade-off parameter 0≦α≦1 that the user can adjust to control the numberof added nodes and edges. At α=0 no nodes or edges are added. At α=1sufficiently many nodes and edges are added to make the distributionsmatch.

FIG. 7 plots the number of nodes of each type that are added to thegraph as α is varied from 0 to 1. The plot on the left shows the numberof nodes of each type that are added to the graph as α (on the x axis)varies from 0 to 1. At α=1 the ratio of nodes of different types in theresulting graph matches that of the training graphs. The plot on theright is the same data but plotted on a log y-axis for clarity.

FIG. 8 shows the mismatch between the circuit's distributions and thetraining data distribution as a function of α. The figure shows thedifference in a current proportion, and that observed in the trainingdata, for each node type, as a (on the x axis) varies from 0 to 1. Atα=1 the ratio of nodes of different types in the resulting graph(circuit design) matches that of the training data, and the differenceis 0. It can be seen from this figure that a large portion of thedistribution mismatch can be accounted for by an optimisation parameterthat is as small as 0.1. At α=0.1 we would only be adding 44 extra nodesto the circuit in our running example.

It is also possible to assign a score to a future-ready circuit via amodel-based approach, in other words by estimating the likelihood of afuture-ready circuit being able to implement the netlists given in thetraining data. This can be done by creating a probabilistic model of thetraining netlists, for example with the use of a probabilistic model ofgraph structure.

The initial placement of nodes on an underlying grid is typicallyarbitrary. It can either be done randomly, or via a more sophisticatedapproximation of the final layout objectives—for example bi-partitioningto minimise the spread of connections.

In addition, as an optional feature, a “layout objective function” maybe used to optimise the layout and/or pipelining of the circuit. Thismay be a different objective function from the objective function thatis optimised in the design of the future-ready circuit, which may alsobe referred to as a “circuit design objective function”.

The layout objective function may be any quantitative measure of desiredcharacteristics or parameters of a circuit layout or pipeline. It can beoptimised without changing the value of the circuit design objectivefunction. One example of a layout objective function is the criticalpath, being the delay of the longest path in a circuit. Optimising thecritical path means that the path length and therefore delay isminimised.

In one embodiment, the critical paths of each input netlist areidentified. The circuit design generator begins by optimising thecritical path. Nodes are rearranged on the grid so as to minimise thedistance between those that lie on the critical paths of each target andtraining applications. During this procedure more weight may be given tothe target data over the training data, so that core functionality isalways assured, while the target data functionality is also notcompromised.

The circuit design generator starts with the netlist that has thelongest critical path and minimises the distance between its nodes. Itcontinues iteratively and in a decreasing order of critical path lengthwith the other applications. Once this is done, the next n longest pathsin each application are calculated to check that the critical pathbefore rearrangement is still the critical path afterwards. If that isthe case, the optimisation is complete. Otherwise, the process isrepeated with the new critical path. The process continues until allcritical paths are optimised or for a predetermined number ofiterations. The number, n, of next longest paths which are checked ineach application after the first pass can be set by a user and can beany value up to the entire number of paths. If they do not select aparticular value for n it can be set at a default value. An exampleuseful default value for n would be three, although of course otherdefault values may be used.

More generally, this optimisation procedure can be formulated as aninstance of an Integer Linear Program (ILP), where the variables beingoptimised over are the assignments of nodes to positions on a grid.Although ILP is in general NP-complete, the routine described aboverepresents an efficient heuristic algorithm.

The output of the circuit design generator is a circuit design thatimplements the target applications, whilst maximising the objectivefunction given the constraints and with a minimal area and costoverhead. This design will be more flexible than a normal ASIC, but lessflexible than a normal FPGA. Its position on this spectrum offlexibility will depend on the parameters chosen by the designer.

There are several options of how register cells can be distributed in anarray generated by the circuit design generator. The register cells maybe treated as any other cell and distributed in the array with each nodehaving its own crossbar interconnect, as shown in FIG. 9. Here, acrossbar interconnect 900 comprising a multiplexer is provided at theedge of a node 902 which may be a register cell. The size of themultiplexer at the input of each node would depend on the number ofother nodes in the array which are to be connected to that node.

Alternatively, the register cells can be integrated into theinterconnect of other larger nodes at the input of the node (as shown inFIG. 10) or at inputs of the interconnect itself (as shown in FIG. 11).

In the arrangement of FIG. 10, a crossbar interconnect 1000 is providedwith an integrated register cell 1004 at an input of the node 1002. Aregister cell 1004 can be implemented within the interconnect of anynode as such. Depending on the configuration of an application, theregister cell 1004 may be selected to be within or not within thedatapath.

In the arrangement of FIG. 11, a crossbar interconnect 1100 is providedwith integrated register cells 1104 at interconnect inputs. The registercell or cells could be placed before the interconnect multiplexer. Thiscould result in increased area due to possibly needing more registercells and their corresponding multiplexer and configuration bits.However, it would reduce the delay in the case where two non-registercells are to be connected directly. The circuit design generator canmake a decision about which configuration to select depending on themain constraint.

FIG. 12 shows the effect on the connection graph of using registers asindependent nodes versus integrating them within the crossbarinterconnect of other larger nodes. The circuit design generator canprovide a user with the option to allow integrating small nodes such asregister cells (and other cells the user labels to be small) into otherlarger nodes.

FIG. 12 illustrates how two datapaths from two different sets oftraining data can be merged. According to a first option, each node inthe datapath is treated as a separate cell with its own interconnectbuilt around it which allows it to connect to any number of other nodes.As a result, the connection graph shows three nodes with two types(arithmetic logic unit (alu) and register (reg) and the connectionsbetween them which would allow both input datapaths to be executeddepending on the interconnect configuration.

According to a second option, small node integration is turned on. Onlytwo nodes are generated and a register cell (reg) is integrated withinone of the nodes. The result is an array with reduced area and delay,which still allows both input datapaths to be executed.

Pipelining of configuration contexts that persist for many iterations(termed ‘kernels’) takes place at the compiler level. The compiledtarget and training netlists may have provisions for pipeline registersand the circuit design generator may use that data to generate a circuitwhere the target netlists will be pipelined.

The circuit design generator may optimise the pipelining by taking intoaccount node positions and wire delays. This method is best suited whenusing individual registers as separate nodes in the circuit's array.

FIG. 13 shows register nodes 1300 that are connected in series to form achain that is present in pipelined datapaths of certain applications.The circuit design generator may create one or more first in first out(FIFO) cells to allow that datapath to be implemented on theFuture-Ready array. The FIFO cell can have several outputs taken fromthe different levels that need to be taken to. The decision of how manyFIFO cells to make, namely whether to tend towards having many smallFIFO nodes or to tend towards having one or few large FIFO nodes,depends on the constraints set by the user.

Several variations are constructed and the best option that can also beoptimised to the constraints will be selected. If delay is to beoptimised, several options are selected and after the last stage, wherecells are moved to minimise the distance between them is made, the bestoption is selected.

There are several ways the circuit design generator may deal with longregister chains used in pipelined datapaths. Some are illustrated inFIG. 14, which shows connection options for two example datapaths shownin FIG. 14—datapath 1 which comprises a chain of three register cells,and datapath 2 which comprises a chain of five register cells.

In a first implementation option, each register may be treated like anyother node type, that is, as a separate node with its own crossbarinterconnect.

In a second implementation option, the registers may be integrated withother cells. This is possible for small chains of registers (say, fewerthan three) but becomes impractical for larger chains. The user has theoption of defining a maximum number of registers in a chain that areallowed to be integrated into a cell.

In a third implementation option (1400 in FIG. 14), each chain ofregisters is grouped into a larger node. This may be a FIFO which hasits own crossbar interconnect multiplexer at its input.

In a fourth implementation option (1402 in FIG. 14), the two datapathsmay be merged into one programmable FIFO with multiple outputs. Eachoutput from the programmable FIFO would be connected to the respectivenode depending on the input datapaths.

In a fifth implementation option (1404 in FIG. 14), each chain ofregisters is grouped into a larger node as in the fourth implementationoption, but additional hardware is provided to limit the programmableFIFO to only one output.

The circuit design generator may choose the option or options that wouldbest achieve the constraints. For example, the user can put a limit onthe number of fan-out from any given node. This would favour the fifthimplementation option over the fourth implementation option. The usercan also activate or deactivate an option.

Various modifications and improvements can be made to the above withoutdeparting from the scope of the disclosure.

For example, the circuit design generator may use a compiler thatcompiles applications onto a coarse-grained fabric and generatesinterconnects based on the crossbar system. However it can also beadapted to work with other compilers to allow for fine-grained cells andalso to allow for the use of other interconnect schemes such asisland-based interconnects.

Groupings of alternative embodiments, elements, or steps of the presentinvention are not to be construed as limitations. Each group member maybe referred to and claimed individually or in any combination with othergroup members disclosed herein. It is anticipated that one or moremembers of a group may be included in, or deleted from, a group forreasons of convenience and/or patentability. When any such inclusion ordeletion occurs, the specification is deemed to contain the group asmodified thus fulfilling the written description of all Markush groupsused in the appended claims.

Unless otherwise indicated, all numbers expressing a characteristic,item, quantity, parameter, property, term, and so forth used in thepresent specification and claims are to be understood as being modifiedin all instances by the term “about.” As used herein, the term “about”means that the characteristic, item, quantity, parameter, property, orterm so qualified encompasses a range of plus or minus ten percent aboveand below the value of the stated characteristic, item, quantity,parameter, property, or term. Accordingly, unless indicated to thecontrary, the numerical parameters set forth in the specification andattached claims are approximations that may vary. At the very least, andnot as an attempt to limit the application of the doctrine ofequivalents to the scope of the claims, each numerical indication shouldat least be construed in light of the number of reported significantdigits and by applying ordinary rounding techniques. Notwithstandingthat the numerical ranges and values setting forth the broad scope ofthe invention are approximations, the numerical ranges and values setforth in the specific examples are reported as precisely as possible.Any numerical range or value, however, inherently contains certainerrors necessarily resulting from the standard deviation found in theirrespective testing measurements. Recitation of numerical ranges ofvalues herein is merely intended to serve as a shorthand method ofreferring individually to each separate numerical value falling withinthe range. Unless otherwise indicated herein, each individual value of anumerical range is incorporated into the present specification as if itwere individually recited herein.

The terms “a,” “an,” “the” and similar references used in the context ofdescribing the present invention (especially in the context of thefollowing claims) are to be construed to cover both the singular and theplural, unless otherwise indicated herein or clearly contradicted bycontext. Further, ordinal indicators—such as “first,” “second,” “third,”etc.—for identified elements are used to distinguish between theelements, and do not indicate or imply a required or limited number ofsuch elements, and do not indicate a particular position or order ofsuch elements unless otherwise specifically stated. All methodsdescribed herein can be performed in any suitable order unless otherwiseindicated herein or otherwise clearly contradicted by context. The useof any and all examples, or exemplary language (e.g., “such as”)provided herein is intended merely to better illuminate the presentinvention and does not pose a limitation on the scope of the inventionotherwise claimed. No language in the present specification should beconstrued as indicating any non-claimed element essential to thepractice of the invention.

Specific embodiments disclosed herein may be further limited in theclaims using consisting of or consisting essentially of language. Whenused in the claims, whether as filed or added per amendment, thetransition term “consisting of” excludes any element, step, oringredient not specified in the claims. The transition term “consistingessentially of” limits the scope of a claim to the specified materialsor steps and those that do not materially affect the basic and novelcharacteristic(s). Embodiments of the present invention so claimed areinherently or expressly described and enabled herein.

It should be understood that the logic code, programs, modules,processes, methods, and the order in which the respective elements ofeach method are performed are purely exemplary. Depending on theimplementation, they may be performed in any order or in parallel,unless indicated otherwise in the present disclosure. Further, the logiccode is not related, or limited to any particular programming language,and may comprise one or more modules that execute on one or moreprocessors in a distributed, non-distributed, or multiprocessingenvironment.

The methods as described above may be used in the fabrication ofintegrated circuit chips. The resulting integrated circuit chips can bedistributed by the fabricator in raw wafer form (that is, as a singlewafer that has multiple unpackaged chips), as a bare die, or in apackaged form. In the latter case, the chip is mounted in a single chippackage (such as a plastic carrier, with leads that are affixed to amotherboard or other higher level carrier) or in a multi-chip package(such as a ceramic carrier that has either or both surfaceinterconnections or buried interconnections). In any case, the chip isthen integrated with other chips, discrete circuit elements, and/orother signal processing devices as part of either (a) an intermediateproduct, such as a motherboard, or (b) an end product. The end productcan be any product that includes integrated circuit chips, ranging fromtoys and other low-end applications to advanced computer products havinga display, a keyboard or other input device, and a central processor.

1. A method of generating a circuit design comprising receiving targetdata and training data; and generating a circuit design for implementingthe target data which is over-provisioned with respect to the targetdata according to the training data.
 2. The method of claim 1, whereinan objective function is received and said over-provisioned circuitdesign optimises the objective function.
 3. The method of claim 1,comprising gathering statistics from the training data and determiningadded cell types and/or connection patterns which over-provision thecircuit design with respect to the training data.
 4. The method of claim1, comprising generating a circuit design for implementing the targetdata and then modifying that circuit design to derive theover-provisioned circuit design.
 5. The method of claim 1, comprisingreceiving one or more physical design constraints and where saidgenerated circuit design is made to comply with the physical designconstraints, wherein the physical design constraints comprise one ormore of the area, delay (speed), or power consumption of the circuit. 6.The method of claim 5, wherein the physical characteristics of differentcells and different interconnects pre-calculated and stored, and usedfor ensuring said generated circuit design complies with the physicaldesign constraints.
 7. The method of claim 1, wherein the target datacomprises netlists, and said netlists are merged to find a minimumnumber of nodes required to run the applications of the target datatogether with a corresponding interconnect layout, to provide a circuitdesign for implementing the target data.
 8. The method of claim 7,wherein merging the netlist comprises inputting a first netlist and asecond netlist, determining the smallest compound netlist that canimplement both the input netlists; and repeating in cases where thereare two or more netlists.
 9. The method of claim 4, wherein nodes andedges are added to the circuit design for implementing the target datain order to generate the over-provisioned circuit design.
 10. The methodof claim 9, wherein a user can specify a node trade-off parameter forcontrolling the number of added nodes, adjustable between a firstextreme where no additional nodes are added and a second extreme wherethe distribution of the nodes and edges in the candidate circuit designexactly matches the distribution of the nodes and edges in the trainingdata.
 11. The method claim 1, wherein nodes in the generated circuitdesign are placed to minimise the distance between those that lie on thecritical paths of each target and training data.
 12. The method of claim11, wherein the nodes are placed according to an integer linear program,in which the variables being optimised are assignments of nodes to aposition in a grid.
 13. The method of claim 11, wherein placing thenodes comprises identifying the critical paths of each input netlist,minimising a distance between nodes for the netlist having the longestcritical path; and repeating iteratively for successive netlists indecreasing order of critical path length.
 14. The method of claim 13,wherein a number of the next longest paths in each application arecalculated to check that the critical path before rearrangement is stillthe critical path; and the node placement is finalised if the criticalpath before rearrangement is still confirmed as the critical path. 15.The method of claim 14, wherein the number of the next longest paths isa user-adjustable parameter.
 16. The method of claim 1, wherein registernodes are formed as independent nodes in an array of the generatedcircuit design; or as integrated components of other logic nodes. 17.The method of claim 1, wherein the generated circuit design comprisespipelined netlists.
 18. The method of claim 17, wherein the pipeliningis optimised by taking into account node positions and wire delays. 19.A circuit design generator arranged to receive target data and trainingdata; and to generate a circuit design for implementing the target dataand which is over-provisioned with respect to the target data accordingto the training data.
 20. A computer program product comprisinginstructions that when executed by a computer cause it to function asthe circuit design generator of claim 19.